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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsCybersecurity increasingly depends on judging what—and whom—to trust: AI systems and their data, software and suppliers, and the messages or identities people encounter online. No single score or automated check can settle that question. A defensible decision uses evidence about security, considers the harm a compromise could cause, and is revisited as systems and threats change.
What does “knowing what to trust” mean in cybersecurity?
Here, trust is a practical assessment, not a guarantee or a formal label. It asks whether a system, service, supplier, message, or claimed identity is reliable enough for a particular use—and what safeguards are needed if it is not.
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That assessment has three connected parts: the security of AI systems and the data they use; the dependability of software, suppliers, and digital services; and the authenticity of communications and identities. A weakness in one layer can undermine the others. For example, a secure internal system may still be exposed through a compromised software dependency, while a convincing fake message can prompt someone to disclose credentials or authorize a harmful action.
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The National Institute of Standards and Technology (NIST) puts the AI issue plainly: “The trustworthiness of AI technologies depends in part on how secure they are.” AI systems share confidentiality, integrity, and availability risks with other software, but their changing attack surface can introduce threats that existing frameworks do not fully address. AI may help defenders and also strengthen attackers’ capabilities. NIST’s AI security and resilience overview describes this as an active, rapidly changing area.
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Why are trust decisions becoming harder?
AI and its data expand the security question
Assessing an AI system means looking beyond whether its output seems plausible. Organizations also need to consider how the system and its data are protected, how it is deployed, and what could happen if its confidentiality, integrity, or availability is compromised. Security evidence must fit the specific system and use; a general claim that AI is “safe” cannot establish that.
Dependencies can reach beyond an organization’s control
Organizations rely on software, suppliers, and digital services they do not build or operate themselves. A weakness or compromise in a dependency can become a risk to the organization using it. NIST’s foundational SP 800-161 Revision 1, Update 1 addresses cybersecurity supply-chain risk management (C-SCRM), including identifying, assessing, and mitigating risks throughout an organization and its supply chain.
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NIST’s C-SCRM resources also list SP 1326 and SP 800-18 Revision 2 among releases in 2026. The broader lesson is that supplier and dependency risk is not a one-time procurement check: it calls for organization-wide attention and reassessment as dependencies and circumstances change. NIST’s C-SCRM resources provide the supply-chain context.
Communications and identities can be manipulated
Cybersecurity is not only about protecting machines. Social engineering and information manipulation target people’s decisions, while deepfakes can make fabricated audio or video appear credible. The European Union Agency for Cybersecurity (ENISA) includes information manipulation and interference, social engineering, and supply-chain attacks in its 2026 threat landscape; it also identifies AI-enabled disinformation and deepfakes among current trends. This makes it important to assess whether a communication or identity is authentic before acting on it.
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What do recent threat and executive reports show?
ENISA’s 2026 Threat Landscape analyzes events observed from 1 January through 31 December 2025. Its findings are EU-focused and should not be treated as a global ranking. ENISA’s Foresight 2030 list includes software-dependency supply-chain compromise, advanced disinformation or influence operations, and abuse of AI as emerging threat categories. Foresight identifies areas of concern; it does not predict that every threat will affect every organization in the same way.
Business surveys offer a different kind of evidence: what executives say they prioritize and how capable they believe their organizations are. PwC’s 2026 Global Digital Trust Insights surveyed 3,887 business and technology executives across 72 countries. In that survey, 60% ranked cyber risk investment among their top three strategic priorities in response to geopolitical uncertainty, while only 6% said their organization was very capable across all vulnerabilities surveyed. These are executive responses—not breach rates or independent measurements of security. PwC’s 2026 Global Digital Trust Insights provides the survey context.
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PwC also reports that knowledge and skills gaps were the top two barriers to implementing AI for cyber defense over the previous year. For the following 12 months, 53% of surveyed executives prioritized AI and machine-learning tools among their top three approaches to cyber talent gaps; specialized managed services were also being prioritized. The same report describes security leaders’ interest in agentic AI for areas including cloud security, data protection, and cyber defense operations. These findings describe reported intentions, not proof that a particular tool, service, or deployment works effectively. PwC’s report discussion of cyber talent and AI priorities covers those findings.
How can an organization assess what to trust?
A useful assessment ties the evidence to the system’s role and the consequences of failure. NIST’s C-SCRM approach centers on identifying, assessing, and mitigating risks across the organization and its supply chain; the same risk-based logic helps structure decisions about AI systems, suppliers, and services.
- Define what is being assessed. Identify the AI system, data, software component, supplier, digital service, communication, or identity claim. Clarify how it is used and what depends on it.
- Describe the consequence of compromise. Consider what could happen if confidentiality, integrity, or availability were lost, a dependency were compromised, or a false message were accepted. Give higher scrutiny to decisions or systems where the potential harm is greater.
- Examine the available security evidence. Ask what is known about protections, data handling, deployment conditions, dependencies, and how the system or supplier is monitored. Separate verifiable evidence from assurances or assumptions.
- Check the ability to detect and respond. An assessment is incomplete if the organization cannot notice a change or compromise and act on it. Consider monitoring, response ownership, and whether relevant expertise is available internally or through managed support.
- Include the full lifecycle and supply chain. Review dependencies beyond the direct vendor relationship, and revisit the assessment when systems, suppliers, use, or threat conditions change.
The result is not a universal verdict that something is “trusted.” It is a decision about acceptable use under stated conditions, with safeguards and a plan to reassess when the evidence changes.
Quick Recap
What should people and organizations avoid assuming?
- AI output is not proof of truth or security. Plausible output does not establish that the system or its data are secure.
- A supplier relationship does not remove dependency risk. Services and software can expose an organization to risks outside its direct control.
- Realistic media does not authenticate a person or request. Social engineering and AI-enabled deepfakes make independent verification important before consequential action.
- Survey confidence is not an objective security rating. PwC’s percentages report the views and priorities of surveyed executives, not universal rates of protection or vulnerability.
- Buying or deploying AI does not by itself resolve a skills gap. The cited survey reports organizational priorities, not evidence that any specific capability or provider is effective. Human expertise and operational capacity remain necessary.
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